Metabolic Reprogramming and Risk Stratification of Hepatocellular Carcinoma Studied by Using Gas Chromatography–Mass Spectrometry-Based Metabolomics

Hepatocellular carcinoma (HCC) displays a high degree of metabolic and phenotypic heterogeneity and has dismal prognosis in most patients. Here, a gas chromatography–mass spectrometry (GC-MS)-based nontargeted metabolomics method was applied to analyze the metabolic profiling of 130 pairs of hepatocellular tumor tissues and matched adjacent noncancerous tissues from HCC patients. A total of 81 differential metabolites were identified by paired nonparametric test with false discovery rate correction to compare tumor tissues with adjacent noncancerous tissues. Results demonstrated that the metabolic reprogramming of HCC was mainly characterized by highly active glycolysis, enhanced fatty acid metabolism and inhibited tricarboxylic acid cycle, which satisfied the energy and biomass demands for tumor initiation and progression, meanwhile reducing apoptosis by counteracting oxidative stress. Risk stratification was performed based on the differential metabolites between tumor and adjacent noncancerous tissues by using nonnegative matrix factorization clustering. Three metabolic clusters displaying different characteristics were identified, and the cluster with higher levels of free fatty acids (FFAs) in tumors showed a worse prognosis. Finally, a metabolite classifier composed of six FFAs was further verified in a dependent sample set to have potential to define the patients with poor prognosis. Together, our results offered insights into the molecular pathological characteristics of HCC

Metabolic profiling analysis of Siraitia grosvenorii revealed different characteristics of green fruit and saccharified yellow fruit

Siraitia grosvenorii is an economic and medicinal plant, its fruit is considered to be good to health for its diverse bioactive ingredients. However, the clarification of chemical composition and their changes after saccharification procedure are not well performed. In present study, a metabolomics method based on ultra-high-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry was developed for metabolic profiling acquisition ofSiraitia grosvenorii extract. Furthermore, information dependent analysis (IDA) combined with self-constructed LC MS/MS identification system for metabolites were employed to identify primary and secondary metabolites in Siraitia grosvenorii. A total of 126 metabolites were identified or tentatively identified. The obvious differences of metabolic profiling between green fruit and saccharified yellow fruit were observed, and metabolites showed their own distribution characteristics in peel, flesh and seed. The majority of the nutrients and effective components were more distributed in flesh and peel, and saccharification was conducive to accumulation of sweet glycosides. This study not only expanded metabolite composition information of Siraitia grosvenorii, but also specified distribution characteristics of identified metabolites. (C) 2017 Elsevier B.V. All rights reserved

Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus

Introduction Despite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics approach to detect DR among patients with type 2 diabetes mellitus (T2DM) efficiently.Research design and methods In this propensity score matching-based case-control study, we used ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system for serum metabolites assessment of 69 pairs of patients with T2DM with DR (cases) and without DR (controls). Comprehensive analysis, including principal component analysis, orthogonal partial least squares discriminant analysis, generalized linear regression models and a 1000-times permutation test on metabolomics characteristics were conducted to detect candidate MDNBs depending on the discovery set. Receiver operating characteristic analysis was applied for the validation of capability and feasibility of MDNBs based on a separate validation set.Results We detected 613 features (318 in positive and 295 in negative ESI modes) in which 63 metabolites were highly relevant to the presence of DR. A panel of MDNBs containing linoleic acid, nicotinuric acid, ornithine and phenylacetylglutamine was determined based on the discovery set. Depending on the separate validation set, the area under the curve (95% CI), sensitivity and specificity of this MDNBs system were 0.92 (0.84 to 1.0), 96% and 78%, respectively.Conclusions This study demonstrates that metabolomics-based MDNBs are associated with the presence of DR and capable of distinguishing DR from T2DM efficiently. Our data also provide new insights into the mechanisms of DR and the potential value for new treatment targets development. Additional studies are needed to confirm our findings

Correlation of preoperative CT imaging shift parameters of the lateral plateau with lateral meniscal injury in Schatzker IV-C tibial plateau fractures

Abstract Background Schatzker IV-C is a high-energy tibial plateau fracture often accompanied by lateral meniscus injuries. While imaging examinations are routine preoperative measurements, the correlation between CT imaging shift parameters of the lateral plateau and lateral meniscal injury in Schatzker IV-C fractures remains uncovered. Methods This retrospective study enrolled a total of 60 patients with Schatzker IV-C tibial plateau fractures at the First People’s Hospital of Hefei. Prior to surgery, CT imaging was used to measure the numerical values of lateral plateau depression (LPD) and lateral plateau widening (LPW). The degree of lateral meniscus injury was confirmed based on intraoperative direct vision, with patients being classified into meniscus injury and non-meniscus injury groups. Dichotomous logistic regression was employed to evaluate the correlation between LPD, LPW, and lateral meniscus injury, while the optimal cut-off points for predicting lateral meniscal injury with LPD and LPW were determined using receiver operator characteristic (ROC) curves. Results The meniscus injury group exhibited a mean LPD of 15.3 ± 3.5 mm, which was significantly higher than the non-meniscus injury group’s mean LPD of 8.4 ± 3.4 mm (P < 0.05). Similarly, the meniscus injury group had a larger mean LPW of 9.4 ± 1.8 mm compared to the non-meniscus injury group’s mean LPW of 6.9 ± 0.9 mm (P < 0.05). The optimal cut-off points for predicting lateral meniscal injury were determined to be 8.40 mm for LPD (with a sensitivity of 95%, specificity of 85%, and AUC of 0.898) and 7.90 mm for LPW (with a sensitivity of 75%, specificity of 90%, and AUC of 0.897). Conclusions Patients with Schatzker IV-C tibial plateau fractures are at a significantly higher risk of lateral meniscal injury when the LPD exceeds 8.40 mm and/or the LPW exceeds 7.90 mm. Our results may provide novel reference metrics for the early diagnosis of lateral meniscal injury in Schatzker IV-C tibial plateau fracture patients when the MRI examination is not available

Metabolic Reprogramming and Risk Stratification of Hepatocellular Carcinoma Studied by Using Gas Chromatography–Mass Spectrometry-Based Metabolomics

Hepatocellular carcinoma (HCC) displays a high degree of metabolic and phenotypic heterogeneity and has dismal prognosis in most patients. Here, a gas chromatography–mass spectrometry (GC-MS)-based nontargeted metabolomics method was applied to analyze the metabolic profiling of 130 pairs of hepatocellular tumor tissues and matched adjacent noncancerous tissues from HCC patients. A total of 81 differential metabolites were identified by paired nonparametric test with false discovery rate correction to compare tumor tissues with adjacent noncancerous tissues. Results demonstrated that the metabolic reprogramming of HCC was mainly characterized by highly active glycolysis, enhanced fatty acid metabolism and inhibited tricarboxylic acid cycle, which satisfied the energy and biomass demands for tumor initiation and progression, meanwhile reducing apoptosis by counteracting oxidative stress. Risk stratification was performed based on the differential metabolites between tumor and adjacent noncancerous tissues by using nonnegative matrix factorization clustering. Three metabolic clusters displaying different characteristics were identified, and the cluster with higher levels of free fatty acids (FFAs) in tumors showed a worse prognosis. Finally, a metabolite classifier composed of six FFAs was further verified in a dependent sample set to have potential to define the patients with poor prognosis. Together, our results offered insights into the molecular pathological characteristics of HCC

Global Metabolic Profiling Identifies a Pivotal Role of Proline and Hydroxyproline Metabolism in Supporting Hypoxic Response in Hepatocellular Carcinoma

Purpose: Metabolic reprogramming is frequently identified in hepatocellular carcinoma (HCC), which is the most common type of liver malignancy. The reprogrammed cellular metabolisms promote tumor cell survival, proliferation, angiogenesis, and metastasis. However, the mechanisms of this process remain unclear in HCC